ner and pos when nothing is capitalized

no code implementations • IJCNLP 2019 • Stephen Mayhew, Tatiana Tsygankova, Dan Roth

While prior work and first impressions might suggest training a caseless model, or using a truecaser at test time, we show that the most effective strategy is a concatenation of cased and lowercased training data, producing a single model with high performance on both cased and uncased text.

Machine Translation named-entity-recognition +6

BSNLP2019 Shared Task Submission: Multisource Neural NER Transfer

no code implementations • WS 2019 • Tatiana Tsygankova, Stephen Mayhew, Dan Roth

This paper describes the Cognitive Computation (CogComp) Group{'}s submissions to the multilingual named entity recognition shared task at the Balto-Slavic Natural Language Processing (BSNLP) Workshop.

Multilingual Named Entity Recognition named-entity-recognition +2

Building Low-Resource NER Models Using Non-Speaker Annotation

no code implementations • 17 Jun 2020 • Tatiana Tsygankova, Francesca Marini, Stephen Mayhew, Dan Roth

In low-resource natural language processing (NLP), the key problems are a lack of target language training data, and a lack of native speakers to create it.

Low Resource Named Entity Recognition named-entity-recognition +2

Building Low-Resource NER Models Using Non-Speaker Annotations

no code implementations • NAACL (DaSH) 2021 • Tatiana Tsygankova, Francesca Marini, Stephen Mayhew, Dan Roth

In low-resource natural language processing (NLP), the key problems are a lack of target language training data, and a lack of native speakers to create it.

Low Resource Named Entity Recognition named-entity-recognition +2